Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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Optimization of the fuzzy model using the clustering and hybrid algorithms

클러스터링 및 하이브리드 알고리즘을 이용한 퍼지모델의 최적화

  • Park, Byoung-Jun (Division of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Yoon, Ki-Chan (Division of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Oh, Sung-Kwun (Division of Electrical and Electronic Engineering, Wonkwang Univ.) ;
  • Jang, Seong-Whan (Division of Electrical and Electronic Engineering, Wonkwang Univ.)
  • 박병준 (원광대학교 전기전자공학부) ;
  • 윤기찬 (원광대학교 전기전자공학부) ;
  • 오성권 (원광대학교 전기전자공학부) ;
  • 장성환 (원광대학교 전기전자공학부)
  • Published : 1999.07.19
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Abstract

In this paper, a fuzzy model is identified and optimized using the hybrid algorithm and HCM clustering method. Here, the hybrid algorithm is carried out as the structure combined with both a genetic algorithm and the improved complex method. The one is utilized for determining the initial parameters of membership function, the other for obtaining the fine parameters of membership function. HCM clustering algorithm is used to determine the confined region of initial parameters and also to avoid overflow phenomenon during auto-tuning of hybrid algorithm. And the standard least square method is used for the identification of optimum consequence parameters of fuzzy model. Two numerical examples are shown to evaluate the performance of the proposed model.

Keywords